September 2024 Site occupancy and abundance models for analyzing multiple-visit detection/nondetection data
Huu-Dinh Huynh, Matthew Schofield, Wen-Han Hwang
Author Affiliations +
Ann. Appl. Stat. 18(3): 2424-2443 (September 2024). DOI: 10.1214/24-AOAS1888

Abstract

We propose an enhanced site occupancy model for analyzing ecological detection/nondetection data obtained from multiple visits. The model distinguishes between abundance, occupancy, and detection probabilities. We allow for transient individuals through a community parameter, c, that characterizes the proportion of individuals fixed across visits. This parameter seamlessly transitions from the standard occupancy model (c=0) to the N-mixture model (c=1), enabling a more accurate analysis of site occupancy data. Through theoretical developments and simulation studies, we demonstrate how this model effectively addresses biases inherent in conventional approaches, particularly for c is not at 0 or 1. We apply the model to various datasets of mammal and bird species and compare it to current approaches.

Funding Statement

This research received funding from the National Science and Technology Council of Taiwan.

Acknowledgments

The authors thank Professor Brendan Murphy and the anonymous reviewers for their insightful comments, which have significantly enhanced this manuscript.

Citation

Download Citation

Huu-Dinh Huynh. Matthew Schofield. Wen-Han Hwang. "Site occupancy and abundance models for analyzing multiple-visit detection/nondetection data." Ann. Appl. Stat. 18 (3) 2424 - 2443, September 2024. https://doi.org/10.1214/24-AOAS1888

Information

Received: 1 September 2023; Revised: 1 February 2024; Published: September 2024
First available in Project Euclid: 5 August 2024

Digital Object Identifier: 10.1214/24-AOAS1888

Keywords: N-mixture model , occupancy probability , zero-inflated model

Rights: Copyright © 2024 Institute of Mathematical Statistics

Vol.18 • No. 3 • September 2024
Back to Top